Airborne LiDAR Point Cloud Filtering by a Multilevel Adaptive Filter Based on Morphological Reconstruction and Thin Plate Spline Interpolation

Point cloud filtering is a crucial step in most airborne light detection and ranging (LiDAR) applications. Many filtering algorithms have been proposed, but the filtering effect has some limitations in complex environments. To improve the filtering effect in complex terrain, a multilevel adaptive filter (MAF) combining morphological reconstruction and thin plate spline (TPS) interpolation is proposed. The digital elevation model (DEM) generated in each iteration is used as the marker image for morphological reconstruction to extract ground pixels, and an adaptive residual threshold is achieved by using terrain gradient as a compensation. The benchmark dataset provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) and another LiDAR dataset in northwestern China were used to evaluate the filtering performance of MAF. For the ISPRS benchmark dataset, MAF obtained the lowest average total error (3.72%) and highest average kappa coefficient (87.16%) compared with eight classic filtering algorithms. For the dataset in northwestern China, the DEM generated from the filtering result of MAF obtained higher accuracy than the filtering result of TerraScan. Overall, the MAF achieved promising results without considering the selection of filtering window, which may enhance the robustness and applicability of the algorithm in different environments.